Project Summary
For our network analysis project, we decided to explore the
connections between Spotify artists to understand how musical
collaboration and similarity create networks in the music industry. We
obtained a dataset containing artist nodes and their relationships,
which allowed us to examine collaboration patterns and identify
influential artists who serve as bridges between different musical
communities.
Our analysis focused on visualizing the network structure,
calculating centrality measures to identify key artists, and performing
community detection to understand how artists cluster together. We were
particularly interested in finding out which artists have the most
connections and how the overall network structure reveals patterns in
musical collaboration. Through various visualization techniques and
statistical analysis, we discovered interesting insights about the
connectivity patterns and community structures within the Spotify artist
ecosystem.
Setup and Data Description
# Load all the packages we need for network analysis
library(tidyverse) # for data manipulation and visualization
library(igraph) # main network analysis package
library(ggraph) # for pretty network plots
library(ggrepel) # to avoid overlapping labels
library(knitr) # for nice tables
# library(DT) # for interactive tables - commented out for PDF compatibility
About the Dataset
# Read in our data files
nodes <- read_csv("nodes.csv")
edges <- read_csv("edges.csv")
# Let's see what we're working with
cat("Node column names:\n")
## Node column names:
print(names(nodes))
## [1] "spotify_id" "name" "followers" "popularity" "genres"
## [6] "chart_hits"
cat("\nEdge column names:\n")
##
## Edge column names:
print(names(edges))
## [1] "id_0" "id_1"
This dataset represents a network of Spotify artists where:
- Nodes (vertices): Individual artists on
Spotify
- Edges: Relationships between artists (could
represent collaborations, similar musical styles, or listener
overlap)
- Data Source: The data was collected from Spotify’s
API and artist relationship databases
- Collection Period: Data reflects artist
relationships as of 2023-2024
- Access: Dataset can be found at Spotify Artist Network
Dataset
The network allows us to study how artists connect to each other and
identify important artists who bridge different musical communities.
Data Preprocessing
# First we need to clean up any duplicate artists
id_column <- names(nodes)[1]
cat("Using", id_column, "as our artist ID column\n")
## Using spotify_id as our artist ID column
# Check how many duplicates we have
cat("Total nodes:", nrow(nodes), "\n")
## Total nodes: 156422
cat("Unique nodes:", length(unique(nodes[[id_column]])), "\n")
## Unique nodes: 156320
# Remove duplicates - keep the first occurrence
nodes_clean <- nodes[!duplicated(nodes[[id_column]]), ]
cat("Nodes after cleaning:", nrow(nodes_clean), "\n")
## Nodes after cleaning: 156320
# Make sure all artists in our edge list actually exist in our node list
edge_artists <- unique(c(edges[[1]], edges[[2]]))
node_artists <- nodes_clean[[1]]
# Find any artists mentioned in edges but missing from nodes
missing_in_nodes <- setdiff(edge_artists, node_artists)
cat("Artists in edges but not in nodes:", length(missing_in_nodes), "\n")
## Artists in edges but not in nodes: 6
# Clean edges to only include artists we have data for
edges_clean <- edges[edges[[1]] %in% node_artists & edges[[2]] %in% node_artists, ]
cat("Edges before filtering:", nrow(edges), "\n")
## Edges before filtering: 300386
cat("Edges after filtering:", nrow(edges_clean), "\n")
## Edges after filtering: 300379
Network Creation and Basic Analysis
# Now we can create our network graph object
cat("Building the network graph...\n")
## Building the network graph...
graph <- graph_from_data_frame(d = edges_clean, vertices = nodes_clean, directed = FALSE)
cat("Network created successfully!\n")
## Network created successfully!
# Calculate some important network measures
# Degree centrality shows how many connections each artist has
V(graph)$degree <- degree(graph)
# Community detection to find groups of closely connected artists
communities <- cluster_louvain(graph)
V(graph)$community <- communities$membership
# Check if the network is all connected or has separate pieces
components <- components(graph)
V(graph)$component <- components$membership
# Create a summary table of our network's basic properties
network_stats <- data.frame(
Metric = c("Total Artists", "Total Connections", "Number of Communities",
"Average Connections per Artist", "Network Density", "Number of Components"),
Value = c(
vcount(graph),
ecount(graph),
max(V(graph)$community),
round(mean(V(graph)$degree), 2),
round(edge_density(graph), 4),
max(V(graph)$component)
)
)
kable(network_stats, caption = "Basic Network Statistics")
Basic Network Statistics
| Total Artists |
156320.00 |
| Total Connections |
300379.00 |
| Number of Communities |
4516.00 |
| Average Connections per Artist |
3.84 |
| Network Density |
0.00 |
| Number of Components |
4338.00 |
The network statistics show us the overall structure of our artist
network. The network density tells us how interconnected the artists
are, while the number of communities reveals how many distinct groups of
artists exist.
Network Visualizations
# For better visualization, we'll focus on the most connected artists
min_degree <- 3
high_degree_nodes <- which(V(graph)$degree >= min_degree)
cat("Creating focused view with artists having", min_degree, "or more connections\n")
## Creating focused view with artists having 3 or more connections
cat("This gives us", length(high_degree_nodes), "artists to visualize\n")
## This gives us 30548 artists to visualize
# If we still have too many nodes, we'll take the top ones by degree
if(length(high_degree_nodes) > 1000) {
top_nodes <- order(V(graph)$degree, decreasing = TRUE)[1:1000]
subgraph <- induced_subgraph(graph, top_nodes)
cat("Showing top 1000 most connected artists\n")
} else if(length(high_degree_nodes) > 0) {
subgraph <- induced_subgraph(graph, high_degree_nodes)
} else {
# Fallback: random sample if no highly connected artists
sample_size <- min(500, vcount(graph))
sampled_nodes <- sample(V(graph), sample_size)
subgraph <- induced_subgraph(graph, sampled_nodes)
cat("Using random sample of", sample_size, "artists\n")
}
## Showing top 1000 most connected artists
Main Network Visualization
set.seed(123) # for reproducible layout
# Create our main network visualization
network_plot <- ggraph(subgraph, layout = "fr") +
geom_edge_link(alpha = 0.2, color = "gray", width = 0.5) +
geom_node_point(aes(size = degree, color = as.factor(community)), alpha = 0.7) +
scale_size_continuous(range = c(1, 6), name = "Number of\nConnections") +
scale_color_discrete(name = "Community") +
theme_void() +
theme(legend.position = "bottom") +
labs(title = "Spotify Artist Collaboration Network",
subtitle = paste("Showing", vcount(subgraph), "most connected artists"))
print(network_plot)
This visualization shows how artists cluster into communities (shown
by color) and reveals which artists have the most connections (shown by
node size). The force-directed layout helps us see the natural groupings
in the network.
Simplified Network View
# Sometimes a simpler view is clearer
simple_plot <- ggraph(subgraph, layout = "fr") +
geom_edge_link(alpha = 0.3, color = "lightgray") +
geom_node_point(aes(size = degree), color = "steelblue", alpha = 0.7) +
scale_size_continuous(range = c(2, 8), name = "Connections") +
theme_void() +
labs(title = "Artist Network (Simplified View)")
print(simple_plot)
Highly Connected Artists
# Let's look at just the super-connected artists
highly_connected <- which(V(graph)$degree > 10)
if(length(highly_connected) > 0) {
custom_subgraph <- induced_subgraph(graph, highly_connected)
# Circular layout works well for smaller networks
influential_plot <- ggraph(custom_subgraph, layout = "circle") +
geom_edge_link(alpha = 0.3, color = "darkgray") +
geom_node_point(aes(size = degree, color = as.factor(community)), alpha = 0.8) +
geom_node_text(aes(label = ifelse(degree > 20, name, '')),
size = 2, repel = TRUE, max.overlaps = 10) +
scale_color_viridis_d(name = "Community") +
scale_size_continuous(range = c(3, 10), name = "Connections") +
theme_void() +
labs(title = "Most Influential Artists in the Network",
subtitle = "Artists with more than 10 connections")
print(influential_plot)
} else {
cat("No artists found with more than 10 connections for this visualization\n")
}
Detailed Analysis Results
Most Connected Artists
# Let's see who the most connected artists are
node_metrics <- data.frame(
Artist = V(graph)$name,
Connections = V(graph)$degree,
Community = V(graph)$community,
Component = V(graph)$component
)
# Show the top 20 most connected artists
top_artists <- head(node_metrics[order(-node_metrics$Connections), ], 20)
# Use kable instead of DT for PDF compatibility
kable(
top_artists,
caption = "Top 20 Most Connected Artists",
row.names = FALSE
)
Top 20 Most Connected Artists
| Johann Sebastian Bach |
1781 |
9 |
1 |
| Traditional |
1371 |
9 |
1 |
| Mc Gw |
858 |
26 |
1 |
| MC MN |
632 |
26 |
1 |
| Jean Sibelius |
580 |
9 |
1 |
| Armin van Buuren |
513 |
19 |
1 |
| Gucci Mane |
509 |
19 |
1 |
| Steve Aoki |
498 |
19 |
1 |
| Snoop Dogg |
495 |
19 |
1 |
| Diplo |
494 |
19 |
1 |
| Tiësto |
475 |
19 |
1 |
| A.R. Rahman |
463 |
71 |
1 |
| Mc Delux |
461 |
26 |
1 |
| David Guetta |
452 |
19 |
1 |
| Mc Rd |
440 |
26 |
1 |
| R3HAB |
420 |
19 |
1 |
| John Williams |
415 |
9 |
1 |
| הכוכב הבא |
377 |
42 |
1 |
| Pritam |
375 |
71 |
1 |
| DJ Antoine |
372 |
41 |
1 |
The artists with the most connections are likely to be major
collaborative artists or those who bridge different musical genres.
These “hub” artists play crucial roles in connecting different parts of
the music network.
Connection Distribution
# Look at the overall pattern of connections
ggplot(node_metrics, aes(x = Connections)) +
geom_histogram(bins = 30, fill = "steelblue", alpha = 0.7, color = "white") +
scale_x_log10() +
labs(
title = "Distribution of Artist Connections",
x = "Number of Connections (log scale)",
y = "Number of Artists"
) +
theme_minimal()
This distribution shows us whether we have a “scale-free” network
(common in social networks) where most artists have few connections but
a small number have many connections.
# Save our visualizations for the presentation
ggsave("spotify_network_main.png", plot = network_plot,
width = 12, height = 10, dpi = 300, bg = "white")
ggsave("spotify_network_simple.png", plot = simple_plot,
width = 12, height = 10, dpi = 300, bg = "white")
# Export our analysis results
write_csv(node_metrics, "spotify_artist_metrics.csv")
cat("Saved files:\n")
cat("- Main network visualization\n")
cat("- Simplified network plot\n")
cat("- Artist metrics spreadsheet\n")
Conclusions and Key Findings
Our analysis of the Spotify artist network revealed several important
insights about how artists connect and collaborate in the music
industry. The network contains 1.5632^{5} artists connected through
3.00379^{5} relationships, with an average of 3.84 connections per
artist. We identified 4516 distinct communities, suggesting that artists
naturally cluster into groups that likely represent different genres,
collaborative circles, or regional music scenes.
The most significant finding is the presence of highly connected
“hub” artists who serve as bridges between different musical
communities. These artists play a crucial role in maintaining the
overall connectivity of the network and facilitating cross-genre
collaboration. The visualizations clearly show both the tight-knit
nature of individual communities and the broader network structure that
connects them. This pattern suggests that while artists tend to work
within specific musical circles, there are key individuals who help
connect these circles and enable the flow of musical influence across
different genres and communities.
Network analysis completed on 2025-06-06
